Apparatus and method for segmenting an image

ABSTRACT

A method and system for segmenting a plurality of images. The method comprises the steps of segmenting the image through a novel clustering technique that is, generating a composite depth map including temporally stable segments of the image as well as segments in subsequent images that have changed. These changes may be determined by determining one or more differences between the temporally stable depth map and segments included in one or more subsequent frames. Thereafter, the portions of the one or more subsequent frames that include segments including changes from their corresponding segments in the temporally stable depth map are processed and are combined with the segments from the temporally stable depth map to compute their associated disparities in one or more subsequent frames. The images may include a pair of stereo images acquired through a stereo camera system at a substantially similar time.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No. 13/025,038filed Feb. 10, 2011 to El Dokor et al, titled “Method and Apparatus forPerforming Segmentation of an Image”, which in turn claims the benefitof U.S. Provisional Patent Application Ser. No. 61/379,706 filed Sep. 2,2010 to El Dokor et al., titled “Imaging”, the contents of each of theseapplications being incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates generally to stereo imaging and more particularlyto a new Graphic Processing Unit (GPU)-based stereo algorithm thataddresses many difficulties typically associated with stereo imaging.The algorithm is considered as belonging to a family ofsurface-disparity algorithms, where the scene is treated as a series ofslowly-varying surfaces, with homogenized color/texture. The algorithmalso preferably includes a real-time component, dubbed residual stereocompute, which addresses real-time constraints and the minimization ofcompute load in real-time, by only analyzing changes in the image, asopposed to the entire image. Finally, the algorithm also preferablyaddresses another chronic problem in stereo imaging, aliasing due totexture regions.

BACKGROUND OF THE INVENTION

With the ascent of new parallel computing platforms, such as the use ofGPUs, as presented in NVIDIA: CUDA compute unified device architecture,prog. guide, version 1.1, 2007 and various accelerated processing units(APUs), real-time high-quality stereo imaging has become increasinglyfeasible. GPUs are comprised of a number of threaded Streamingmultiprocessors (SMs), each of which is, in turn, comprised of a numberof streaming processors (SPs), with example architectures presented inDavid Kirk and Wen-Mei W. Hwu, Programming Massively Parallel ProcessorsA Hands-on Approach.: Elsevier, 2010.

The human visual system is very hierarchical, and visual recognition isperformed in layers, first by recognizing the most basic features of animage, and then recognizing higher-level combinations of those features.This process continues until the brain recognizes an adequatelyhigh-level representation of the visual input. FIG. 1 is a diagramillustrating possible different levels in the visual hierarchy as setforth in M. Marszalek and C. Schmid, “Semantic hierarchies for visualobject recognition,” in Proceedings of IEEE Conference on ComputerVision and Pattern Recognition, 2007. CVPR '07, MN, 2007, pp. 1-7. As isshown in FIG. 1, the most basic functions 110 are first recognized by anindividual. Thereafter, higher level patterns and clusters 120 arerecognized from these clusters 110. Moving up the heirarchy, shapes andsegments 130 are recognized from groups of the patterns and clusters.Finally, after many layers of aggregation, one or more complex objects140 may be recognized by the individual.

There are many different approaches to stereo imaging. In accordancewith the present invention, segment-based approaches will be mainlyutilized, and may also be referred to as surface stereo. This is becausesegment-based approaches best resemble the human visual system. Suchalgorithms are ones in which the 3D field-of-view is treated as a set ofsmooth, slowly varying surfaces as set forth in Michael Bleyer, CarstenRother, and Pushmeet Kohli, “Surface Stereo with Soft Segmentation,” inComputer Vision and Pattern Recognition, 2010. Segment-based approacheshave emerged in recent years as an alternative to many region-based andpixel-based approaches and have outperformed in accuracy on theMiddlebury dataset almost any other algorithm. The Middlebury set iswidely considered the reference dataset and metric for stereo/disparitycomputation algorithms as set forth in (2010) Middlebury Stereo VisionPage. [Online]. http://vision.middlebury.edu/stereo/.

There are many reasons why such methods today represent the moredominant approaches in stereo imaging, see Andreas Klaus, Mario Sormann,and Konrad Karner, “Segment-Based Stereo Matching Using BeliefPropagation and a Self-Adapting Dissimilarity Measure,” in Proceedingsof ICPR 2006, 2006, pp. 15-18. Segment-based approaches addresssemi-occlusions very well. They are also more robust to local changes.Other pixel and region-based approaches blur edges, causing ambiguitybetween background and foreground regions, as well as potentiallyremoving smaller objects, as noted in Ines Ernst and Heiko Hirschmuller,“Mutual Information based Semi-Global Stereo Matching on the GPU,” inLecture Notes in Computer Science, vol. 5358, 2008, pp. 228-239. Across-based local approach as set forth in Jiangbo Lu, Ke Zhang,Gauthier Lafruit, and Francky Catthoor, “REAL-TIME STEREO MATCHING: ACROSS-BASED LOCAL APPROACH,” in 2009 IEEE International Conference onAcoustics, Speech and Signal Processing, 2009 represents animplementation of such approaches on the GPU, but is still impracticalbecause it exhibits weaknesses at regions of high texture and regionswith abrupt changes in color/intensity. However, many segment-basedapproaches are therefore tedious, inaccurate and require a significantamount of computation, even on the GPU.

Therefore, it would be beneficial to provide an improved segment-basedapproach that overcomes the drawbacks of the prior art.

SUMMARY OF THE INVENTION

Therefore, in accordance with various embodiments of the presentinvention, an inventive system and method has been developed fordisparity estimation in stereo images associated with current stereoalgorithms. The implementation of a preferred embodiment of theinvention may utilize a GPU or other dedicated processing unit, allowingfor significant improvements in performance, both in accuracy andefficiency, but may be employed on any appropriate computing platform.

In accordance with the various embodiments of the present invention, anovel surface/segment-based approach for computing disparity estimationis provided, a real-time approach to computing stereo on the residualimage as opposed to the entire image is described, and a means foraddressing textured regions, which has been a major drawback of previousstereo algorithms, is finally presented.

Therefore, in accordance with various embodiments of the presentinvention, the following will be presented:

1. Segment-based disparity decomposition: a new technique forsegment-based disparity decomposition, defining a newclustering/segmentation strategy.

2. Segmentation with GPU implementation.

3. Texture-based segmentation and disparity/stereo computation and theconcept of texture emergence with disparity computation

4. GPU-based heterogeneous sorting algorithm, and the underlying conceptof data reduction

5. APU claim for sorting heterogeneous data through the utilization ofthe CPU and a number of ALUs, all sharing the fundamental memoryarchitecture

6. Residual compute stereo. Video encoding scheme for residual compute.

7. Stereo on the Bayer pattern, and only demosaicing the residual image

8. Stereo codec associated with Residual Compute stereo

Implementation may preferably be on a Graphical Processing Unit (GPU),with comparisons being highlighted with existing methods and otherprevalent algorithms for disparity computation. The inventive approachprovides a significant improvement over existing depth estimationalgorithms. Its preferred GPU-based implementation presents a number ofnovel interpretations of former algorithms, as well as realizations ofnew algorithms, ranging from texture segmentation, to disparitycomputation.

Still other objects and advantages of the invention will in part beobvious and will in part be apparent from the specification anddrawings.

The invention accordingly comprises the several steps and the relationof one or more of such steps with respect to each of the others, and theapparatus embodying features of construction, combinations of elementsand arrangement of parts that are adapted to affect such steps, all asexemplified in the following detailed disclosure, and the scope of theinvention will be indicated in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the invention, reference is made tothe following description and accompanying drawings, in which:

FIG. 1 depicts possible human visual hierarchy levels;

FIG. 2 depicts an example stereo board setup in accordance with anembodiment of the invention;

FIG. 3 depicts texture decomposition with a steerable filter bank inaccordance with an embodiment of the invention;

FIG. 4 provides an overview of real-time segmentation in accordance withan embodiment of the invention;

FIG. 5 illustrates the global memory architecture associated with eachpixel in a source image in accordance with an embodiment of the presentinvention;

FIG. 6 highlights global memory architecture specific toresynchronization segmentation in accordance with an embodiment of theinvention;

FIG. 7 highlights global memory architecture specific to residualsegmentation in accordance with an embodiment of the invention;

FIG. 8 depicts computation minimization during the transition fromresynchronization compute to residual compute in accordance with anembodiment of the invention;

FIG. 9 provides a visual overview of residual segmentation in accordancewith an embodiment of the invention;

FIG. 10 defines an overall approach to a stereo decomposition algorithmin accordance with an embodiment of the invention;

FIG. 11 presents an example implementation of residual compute inaccordance with an embodiment of the invention;

FIG. 12 depicts a first stage of statistics accumulation, which occursat an end of an initialization kernel in accordance with an embodimentof the invention;

FIG. 13 depicts a statistics component of a linking kernel in accordancewith an embodiment of the invention;

FIG. 14 depicts a statistics component of the refinement kernel inaccordance with an embodiment of the invention;

FIG. 15 depicts a global merge of a thread index and spatially indexedstatistics buffers in accordance with an embodiment of the invention;

FIG. 16 provides an overview of a disparity decomposition algorithm inaccordance with an embodiment of the invention;

FIG. 17 expands the counting component of disparity decomposition,depicting the accumulation of overlapping pixels in shared memory inaccordance with an embodiment of the invention;

FIG. 18 outlines an implementation of disparity decomposition in CUDA inaccordance with an embodiment of the invention;

FIG. 19 illustrates a clustering of texture in accordance with anembodiment of the invention;

FIG. 20 displays results of disparity decomposition on a checkeredtexture in accordance with an embodiment of the invention; and

FIG. 21 displays results of disparity decomposition on a simulatedtexture region in accordance with an embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

One or more embodiments of the invention will now be described, makingreference to the following drawings in which like reference numbersindicate like structure between the drawings.

In accordance with a particular embodiment of the present invention,FIG. 2 depicts a smart processing board 200 coupled with two imagesensors 210 that are preferably placed with their rows parallel acrossan images' epipolar lines. The sensors are preferably gen-locked so thatthe left and right images are generated at the same time, and calibratedfor stereo. The setup itself may comprise either a simple cameraconnected to a compute device, or a smart camera, in which case, thecompute capabilities may also be present on-board the stereo sensorpair. A detailed description of stereo vision fundamentals is set forthin David A. Forsyth and Jean Ponce, “Stereopsis,” in Computer Vision AModern Approach.: Prentice Hall, 2003.

As is discussed in M. Marszalek and C. Schmid, “Semantic hierarchies forvisual object recognition,” in Proceedings of IEEE Conference onComputer Vision and Pattern Recognition, 2007. CVPR '07, MN, 2007, pp.1-7, a 3D field-of-view is preferably defined as an ensemble of smooth,well-behaved surfaces, varying slowly over space and time. Constancy inregistration lends itself conceptually to spatio-temporal constancy, andallows some simplifying assumptions to be made for scene segmentation,including segment-based robustness as well as integrity across frames.Specifically, if a segment in a reference image is accurately matched toits counterpart in a slave image, then estimating the associateddisparity value becomes significantly simplified. Good segmentation,however, poses many challenges, including context, shading, gradientchanges, etc. Also, correctly matching the segment is no trivial task.Once a segment is robustly tracked in one image, a real challenge arisesin trying to match the tracked segment to a corresponding segment in asecond image To ensure a level of constancy, segmentation has to also bemaintained over a number of frames (time), so that regions are properlytracked. This simplifies the process of firstly computing the correctdisparity, and then tracking the segment's disparity across frames.

The Relevance of Segmentation to Disparity Computation

Segmentation, as part of disparity computation, is very crucial, as itallows for a hierarchical approach to the scene and field-of-view (FOV)organization. This approach has been attempted before, see in David A.Forsyth and Jean Ponce, “Stereopsis,” in Computer Vision A ModernApproach.: Prentice Hall, 2003 and in M. Marszalek and C. Schmid,“Semantic hierarchies for visual object recognition,” in Proceedings ofIEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR'07, MN, 2007, pp. 1-7. Two properties, relevant to segments, which arecarried across scenes are very important: cluster (or segment) number,and the segment's associated first and second-order moments. Thefundamental idea is to track such segments across time (consecutiveframes) and across space (reference and left frames). The two propertiestogether comprise spatio-temporal constancy.

Governing Principles of Disparity Generation

Initially, a simplifying assumption may be made that entire segmentsshare a common disparity, again borrowing aspects of the surface-basedapproach noted in Marszalek noted above. This concept will be utilizedto iteratively refine a disparity estimate of what will be consideredsub-segments of the actual segment. Rigid segmentation may lead to localinaccuracies, and may fundamentally change depth calculation. Theinventive approach in accordance with various embodiments of the presentinvention assumes that an initial disparity estimate provides for acoarse disparity calculation of a given segment. This disparitymeasurement may be allocated to all pixels in the segment, includingones that are semi-occluded (not visible by one of the two sensors).

Spatio-Temporal Segmentation and Tracking

One of the main advantages of attempting segmentation employing a GPU orother appropriate processing platform is the ability forSingle-Instruction-Multiple Data (SIMD) operations to simultaneouslyupdate different regions in an image. This significantly simplifies aset of rules being used during a region-growing phase of segmentation.

The Image as a Cluster Map—Clustering may include one or more aspects asset forth in U.S. patent application Ser. No. 12/784,123, filed May 20,2010, titled “Gesture Recognition Systems and Related Methods”,currently pending, the contents thereof being incorporated herein byreference. Further refinements of this basic system are set forthherein. Therefore, in accordance with embodiments of the presentinvention, the following processes may be employed.

Every pixel in a frame, i, presented as p_(i)(x,y), is assigned acluster number, c_(i)(x,y), such that:c _(i)(x,y)=x·y−y mod x  Equation 1In Equation 1, clusters are sequentially numbered left-to-right, andtop-to-bottom, such that:c _(i)(x,y)=c _(i)(x,y−ε)+ε,  Equation 2where ε is an integer number. At this stage in the segmentationalgorithm, pixels may begin to be connected, such that two pixelsp_(i)(x,y) and p_(i)(x+1,y) are connected if:

$\begin{matrix}{{{{p_{i}\left( {x,y} \right)} - {p_{i}\left( {{x + 1},y} \right)}} \leq \tau_{p}}{where}{{p_{i}\left( {x,y} \right)} = \left\lfloor \begin{matrix}{r_{i}\left( {x,y} \right)} \\{g_{i}\left( {x,y} \right)} \\{b_{i}\left( {x,y} \right)}\end{matrix} \right\rfloor}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

Comprising the three channels, r, g, and b respectively, for a givenframe i. A similar implementation has also been developed for HSV-basedsegmentation. HSV offers a number of perceptually driven properties forsegmentation, which may be useful, especially for mitigating issues withshading. This connectivity assigns to the pixels a cluster number thatis lowest in its neighborhood. As mentioned earlier, priority is set indescending order, from left to right and from top to bottom. Recall theassumption that all pixels belonging to the same surface are at the samedisparity. Surface discontinuities can either refer to a depthdiscontinuity or a color/texture discontinuity.

Implementation in CUDA—In accordance with the various embodiments of theinvention, a key pixel for each cluster is assigned, such that a clusteris defined by that key pixel. Since the implementation ismulti-threaded, each pixel in a cluster therefore points to the samepixel to simplify and homogenized the process of trackingclustered/segmented regions. By pointing to the key pixel, clusterspreferably have two attributes: 1) a cluster number that is associatedwith that key pixel, and 2) a stopping criterion, beyond which thecluster is terminated (see implementation above.) The assumption thatall pixels belonging to the same surface are at the same disparity valueis easily violated without an iterative approach. Specifically, suchsurfaces have to be further segmented into regions, for more accuratedisparity calculation. Surface discontinuities, among other things, canrefer to depth discontinuity or a color/texture discontinuity.

Semi-occlusions, Pixels having more than One DisparityValue—Semi-occluded pixels are described in Dongbo Min and KwanghoonSohn, “Cost Aggregation and Occlusion Handling With WLS in StereoMatching,” IEEE Transactions on Image Processing, vol. 17, pp.1431-1442, 2008, and Vladimir Kolmogorov and Ramin Zabih, “ComputingVisual Correspondence with Occlusions via Graph Cuts,” in InternationalConference on Computer Vision, 2001, as pixels that are visible in onlyone image (from one sensor), but not from the other. Typically, stereoalgorithms have difficulty handling semi-occlusions. In accordance withthe present invention semi-occlusions are defined as locations thatcontain more than one disparity value (a left and right-image disparityvalue), since there is a foreground and a background value associatedwith the pixel location. It has been determined by the inventors of thepresent invention that it is very appropriate in such a case to addresssemi-occlusions with an inspiration from Gestalt—looking at it from ascene organization standpoint. Specifically, if a pixel belongs to asegment, that pixel will take on the segment's disparity value, even ifits actual location is semi-occluded in the reference image. Thisultimately means that there are two, not one, disparity estimates forsemi-occlusions. The one that is relevant to the segment is chosen. Asimple example that is inspired by biology, in which our visual systemsimilarly compensates for semi-occlusions, is illustrated by looking atobjects that are only partially occluded, and yet our eyes are capableof reconstructing the entire object at the correct depth. A whole schoolof psychology has evolved around this principle, known as Gestalttheory, in which our brains are described as having the capacity toreconstruct complex forms, from simpler, and sometimes incomplete,constituents, as further described in Vladimir Kolmogorov, Ramin Zabih,and Steven Gortler, “Generalized Multi-camera Scene Reconstruction UsingGraph Cuts,” in Proceedings of the International Workshop on EnergyMinimization Methods in Computer Vision and Pattern Recognition, 2003.

Slanted Regions-Iterative Segmentation—For spatially-dominant regions,spanning large areas, in accordance with embodiments of the invention,an iteration may be used in which the segment itself is broken up acrossboth the rows and columns, in essence, further segmenting the clustersinto smaller ones, and re-evaluating these smaller clusters. Byenforcing over-segmentation in the disparity space, a more refineddisparity estimate may be obtained. Segmenting clusters across rowsprovides for the vertical tilt (or slant) of a cluster, while segmentingacross the columns provides for the horizontal tilt (or slant) of agiven cluster.

Refining the iteration—Given that iterations may be executed in both thevertical and horizontal directions, these iterations can be combinedtogether to produce one result including both iterations. Onepossibility is to average both iterations, such that the new estimateddisparity is given by Equation 4:{tilde over (d)} _(i)(x,y)=½d _(irows)(x,y)+½d _(icolumns)(x,y)

Segmenting Texture—One of the fundamental drawbacks and argumentsagainst stereo imaging has been the performance in regions that arehighly-textured. Such regions may contribute to false positives in thedisparity computation, since many similarity metrics will confuseregions which look spatially similar. In accordance with embodiments ofthe present invention, texture segmentation may be approachedgeneratively, in which a texture primitive is in itself comprised of anumber of color primitives. First, a look at alternative approaches intexture segmentation is presented to see how they may affectstereo/disparity computation.

Texture, described extensively in the literature, (see Wolfgang Metzger,Laws of Seeing.: MIT Press, 2006), is comprised of a fundamentalbuilding element, called a texton, or texture primitive. Extractingtexture primitives from images is a challenging task and has been thesubject of on-going research. Some approaches utilize a Gabor (orwavelet) multi-scale filter bank to decompose a texture into itsprimitives, establishing spatial periodicity and extracting a textureprimitive that can be explicitly expressed. For example, in Metzger,dominant spatial texture orientations of a grayscale version of an imageare extracted, and multiscale frequency decomposition is attempted. Thisis accomplished with a steerable pyramid decomposition process as setforth in Junqing Chen, Pappas T. N., Mojsilovic A., and Rogowitz B. E.,“Adaptive perceptual color-texture image segmentation,” IEEETransactions on Image Processing, vol. 4, no. 0, pp. 1524-1536, October2005, in which frequency decomposition is accomplished with fourorientation subbands: horizontal, vertical, and the two diagonals, isshown in FIG. 3. Running the input image through the filter bank, themaximum response at each pixel location may be computed. Thiscomputation may be performed by calculating the energy at each pixel,defined as the square of the filter response coefficients. A one leveldecomposition may then be utilized. A pixel location (x,y) may then beclassified as belonging to a given texture orientation depending on themaximum response at (x,y) of the filter bank. A composite texture wouldhave more than one strong response from the filter bank.

Computationally, these are very expensive methods, and they make manyunrealistic assumptions about knowing a priori dominant orientationswhich are associated with a texture primitive. Furthermore, suchapproaches suffer in the presence of a dominant gradient which isassociated with the texture, i.e. if the texture has a gradientcomponent that spatially and/or temporally varies. Such approachesnegatively impact disparity/stereo computation for two reasons: 1) asmentioned above, regions with similar responses to texture decompositionmay behave similarly to a region, or pixel-based similarity metric, and2) texture segmentation and classification may be computationallyprohibitive when combined with stereo, and the rest of the algorithm.

Approaching Texture Segmentation Generatively—Therefore, in accordancewith various embodiments of the present invention, a different approachto texture segmentation is preferably employed that is very useful fordisparity computation, and adopts the concept of “emergence” from theGestalt school of psychology, see Vladimir Kolmogorov, Ramin Zabih, andSteven Gortler, “Generalized Multi-camera Scene Reconstruction UsingGraph Cuts, in Proceedings of the International Workshop on EnergyMinimization Methods in Computer Vision and Pattern Recognition, 2003.In accordance with embodiments of the invention, a texton, or textureprimitive may be viewed as a set of color primitives, combined togetherto produce a perceptually consistent spatially periodic orslowly-varying texture primitive, which, in itself, comprises texture.These color primitives define and comprise the texture spatialprimitives. Many techniques have been developed to evaluate texture. Inaccordance with embodiments of the invention, texture is reviewed in thesense of interleaving a series of color primitives through slowlyvarying them over time. This is a generative approach, in which it isdetermined how a particular texture is formed in the first place, andthen the texture is represented as a weighted linear combination of acolor primitive, as well as its gradient that is associated with thatcolor primitive. Pixels belonging to the same color primitive may beclustered independently, such that, for any given texture, a textureprimitive is defined as a linear combination of clusters, comprised ofthese color primitives. A texture primitive, T, over a window, W, may begiven by:T _(x,y) =T _(x+ε) ₁ _(,y+ε) ₂   Equation 5

Where ε₁ and ε₂ are values which represent periodicity in the textureprimitive.

Also, T is represented by:T _(x,y) =C ₀(x,y)+C ₁(x,y)+ . . . +C _(N-1)(x,y)where∇T _(x,y)=∇_(C) ₀ T _(x,y)(x,y)+∇_(C) ₁ T _(x,y)(x,y)+ . . . +∇_(C)_(N-1) T _(x,y)(x,y)The gradient associated with the texture represents an N-th order tensorfield comprised of the linear combination of all the partials associatedwith the individual segment changes across a given cluster/segment. Onecan look at each segment as representing a path or direction.

To successfully compute disparity for textured regions, the inventiveapproach implicitly mitigates texture by looking at variations in bothscale and intensity, as gradual changes in themselves. Together, changesin color, intensity, and lighting, make extracting a texture primitivequite challenging, mainly due to the permutation of combinations that isperceptually easy to identify, yet very difficult to describe in closedform. Instead, the inventive approach is less concerned with definingtexture primitives and more concerned with generatively reproducingtexture primitives from more fundamental spatially varying primitives.Such primitives constitute interleaving segments that constitute aperceptually-visible texture. This concept extends to address primitivesthat are disjoint, such as those that may include, for example, acheckerboard pattern as shown in FIG. 20. This runs counter to the ideaof the segment being one contiguous set of spatial elements. Such anotion may be violated primarily because once such a segment is robustlyidentified, it is relatively simple to run disparity calculation on thesegment (and hence the number of segments comprising the textureprimitive). Once all the segments' disparity values have been calculated(see discussion of on texture-based disparity decomposition, below),then the actual textured region emerges from the disparity map asbelonging to a consistent disparity, but is spatially segmented into anumber of interleaving segments comprising the texture itself, hence theconcept of emergence, mentioned earlier.

To summarize, the underlying concepts governing texture segmentationinclude 1) Texture color primitives can link up correctly even in thepresence of a gradient associated with them, to form a consistentsegment; and 2) Interleaving two or more such segments togetherconstitutes texture. Segmentation Rules: as set forth in the Ser. No.12/784,123 reference noted above, may include 1) the concept ofspatio-temporal constancy; 2) the lowest cluster number rule; and 3)that segments ultimately live and die in the field-of-view.

Residual Image Compute—Stereo Codec

This is a computationally expensive algorithm that can be significantlyimproved for real-time performance in accordance with variousembodiments of the invention, in which computation is only performed onthe changes in the image, referred to as the residual image. The overallapproach requires a similar one to typical video codec encoding schemes,such as the H.264 encoding standard as presented in W. T. Freeman and E.Adelson, “The design and use of steerable filters,” IEEE Transactions onPattern Analysis and Machine Intelligence, vol. 13, pp. 891-906,September 1991 or Thomas Wiegand, Gary J. Sullivan, Gisle Bjøntegaard,and Ajay Luthra, “Overview of the H.264/AVC Video Coding Standard,” IEEETransactions on Circuits and Systems for Video Technology, vol. 13, no.7, pp. 560-576, July 2003. Such standards have emerged as the newcompression standard associated with video codecs. H.264 hasflexibility, as a video compression standard, which Metzger allows forreal-time gains in bandwidth. H.264 enables this great reduction byexploiting both intra-frame and inter-frame redundancies and reducingthe overall compute load required to represent an image.

Since changes in a video stream are gradual, an image is mostlyunchanged in real-time, transitioning from one frame to another. It islogical to assume in accordance with embodiments of the invention thatthe FLOPS/computational load would be diminished in real-time, sincerelatively very few pixels change between frames. As such, a similarmethod is adopted in accordance with embodiments of the presentinvention where a reference image I is utilized, and only the residualinformation between consecutive images is preferably processed inreal-time. In H.264, an image is segmented into blocks of non-uniformsize. This concept is transferred over in accordance with embodiments ofthe present invention, utilizing an existing segmented cluster mapinstead. However, a more complex memory architecture may also beemployed, comprised of a reference image, a cluster map, a segmentedreference image, and a depth map. In accordance with embodiments of thepresent invention, a reference memory architecture may be utilizedcomprised of all these images, and a residual architecture comprised ofthe difference between these reference images and subsequent images in avideo stream. As is shown in FIG. 4, one or more reference frames 410are shown. In each of these reference frames, a complete segment clustermap is acquired at a resynchronization phase. Each page 420 therebetweencomprises a composite frame comprised of temporally stable segments, andmore recently computed temporally unstable segments.

In accordance with the present invention, a process for computing thevarious required valued resembles that suggested for the H.264 and othervideo encoding schemes. Thus, two main sections are presented: FIG. 6depicts a resynchronization segmentation portion of the processing. Inthe section of the algorithm shown in FIG. 6, a series of steps is usedto create a stable depth map. FIG. 6 highlights the global memoryarchitecture specific to resynchronization segmentation. At 610, apreliminary clustering step is shown, assigning each non-backgroundpixel (525) a cluster number derived from the pixel's spatialcoordinates. At 615, linked pixels are assigned a common cluster number,forming segments that accumulate color and size statistics in twobuffers. At 620, separate statistics buffers are merged together,allowing each segment's average color to be computed at 625. The averagecolor is then used to further refine the segmentation at 625, whereclusters grow agglomeratively according to color and spatialconnectivity. After successive iterations of the clustering process, thefinal statistics for each segment are computed 630.

The term temporally stable is preferably used to describe segments whichhave very little change in their overall statistics and depth values. Inthe section of the algorithm set forth in FIG. 7, a residual computesection looks at the differences between subsequent frames and processesthe residuals associated with these frames, enabling a reduced computeload i.e. less compute operations to perform disparity estimation. FIG.7 highlights the global memory architecture specific to residualsegmentation. Drawing on the segmentation flags defined at 525, residualsegmentation begins at 710 with temporal differencing to determine pixelstability between the current and previous frames. At 715, unstableresidual pixels are assigned new cluster numbers, while key pixels arestabilized, and stable pixels are ignored. At 720, a specialized linkingstage is performed to update transitional pixels with a persistentcluster ID. From this point, segmentation continues as defined at615-625. At 725, the stability of each segment is evaluated to determineif the depth of a segment should be reevaluated.

In accordance with various embodiments of the invention, during theprocess, starting with a reference, I_(c) cluster map (see FIG. 7)derived utilizing the standard algorithm presented above, afterdetermining pixels which have changed they can be properly encoded. Withthat architecture in mind, it is only necessary to examine pixels thatare temporally unstable. Once the cluster map is updated byre-initializing the pixels with initial cluster numbers, the rest of thealgorithm flows from that step, as will be described below.

Associated Pixel Flags and Pixel Memory Architecture—As is next depictedin FIG. 5, an overall architecture of global memory associated withper-pixel allocations is provided. As is shown in FIG. 5, correspondingleft and right framed 510L, 510R are shown. Each such frame comprises aplurality of pixels 515. Each pixel 515 further comprises a plurality ofRGBA channels 520 associated with the source data. In memory, everypixel is comprised of the R,G, and B channels, as well as an alphachannel (shown as RGB and A segments 520). In this implementation, thealpha channel is used to store a number of flags which are useful forresidual compute operations. These segment- and pixel-level encodedflags are shown in FIG. 5 at information 525, as may preferablycomprise:

Segment Stability: denotes the temporal stability of the segment thatthe current pixel is associated with.

Cluster ID Number: denotes the cluster number associated with a thecurrent pixel.

Cluster Affinity: Denotes the relationship of the cluster with a keypixel in another cluster.

Key Pixel: denotes whether the current pixel is a key pixel of a clusteror segment.

Temporal Stability: denotes the temporal stability of the current pixel.

Edge: denotes whether the current pixel is an edge pixel.

Edge: another flag for an edge pixel.

!Background: denotes whether the current pixel is not a backgroundpixel.

Note that pixels are background pixels, temporally stable pixels, ortemporally unstable pixels.

Segmentation in Real-time—Under real-time conditions, segmentation isonly attempted on any temporally unstable residual pixels. Clusteringfollows that step, such that pixels with common color and spatialproximity are clustered together. Agglomeratively connecting suchclusters to previously existing segments is then performed. At thispoint, any clusters/segments whose size/statistics have beensignificantly changed undergo disparity decomposition. Such segments areconsidered unstable, and will have their disparity recomputed.Otherwise, the remaining, stable, segments do not contribute to theresidual data, and are part of the reconstructed frame. Disparitydecomposition is not performed on temporally stable segments, andinstead, a composite depth map is formed, comprised of temporally stable(and thus already pre-computed segments) as well as the computedsegments.

Such an approach to real-time segmentation and depth calculationrequires a memory architecture in global memory to support thisapproach. The architecture proposed in accordance with variousembodiments of the present invention contains both reference as well asresidual portions, again in a manner similar to typical video codecssuch as H.264. This architecture is then utilized at different steps inthe analysis to significantly reduce compute load and enhance accuracy.FIG. 8 depicts computation minimization during the transition fromresynchronization compute 210 to residual compute 220. As is shown inFIG. 8, the computational burden for resynching frames of an image issubstantially higher than that required for the residual computations.

FIG. 9 provides a visual overview of residual segmentation, beginningfrom the source image at 910. As is further shown in FIG. 9, the inputimages shown in row 910 comprise, and may be shown as background pixels,temporally stable non-background pixels (row 920) and temporallyunstable pixels (row 930), all being clustered and segmented into acomposite image shown in row 940. Row 920 highlights the non-residualsegments from the previous frame while row 930 highlights the residualsegments. The output image, at 940, combines the residual andnon-residual segments to create a composite cluster map. Significantgains in compute load are especially useful for enhancing accuracy andenabling iterative updating of depth calculation and refinement, as wellas increasing frame rate.

Thus the following set of guidelines governing segmentation anddisparity computation in real-time are realized. If there is no changein the image, the overall FLOPS count should drop very dramatically. Anysegment that has significantly changed is deemed temporally unstable andwill have its disparity recalculated. The depth map is a composite mapcomprised of temporally stable segments as well as temporally unstableones

Implementation in CUDA—FIG. 10 depicts a high level approach as to howthe algorithm for stereo decomposition with resynchronization compute inaccordance with one or more embodiments of the present invention are tobe implemented. As is shown in FIG. 10, and in keeping with the abovedescription, a resynchronization compute step 1010 is first implemented,followed by a residual recompute step 1020 to update temporally unstablepixels and clusters in a next frame. Finally, a depth refinement stepmay optionally be employed at step 1030.

To implement this approach, the inventive architecture accounts for thenumber of flags noted above that define the stability of the pixels, aswell as their relationship to the corresponding clusters and segments. Apixel's cluster number is then assessed; the pixel either receives a newcluster number (cluster number is elevated) if it is temporallyunstable, or maintains the same cluster number that is associated with asegment. This enables changes at the pixel level to affect the segment.Conversely, if the segment is found to be temporally stable, all pixelsbelonging to that segment will have their temporal stability flagupdated accordingly, even if the pixels are temporally unstable. Thus,to deem a pixel temporally unstable requires that both the segmentstability flag and the pixel stability flag be enabled. Therefore, if asegment is deemed temporally unstable, every pixel in that segment isdeemed unstable in spite of the fact that some of them might have beendeemed stable earlier in the algorithm.

FIG. 11 presents an example of this process. As is shown in FIG. 11, ina first step 1110 a previous cluster map C_(i) from a prior frame iscopied to a current cluster map C_(i-1). In a next step 1120, adifference C_(i)-C_(i-1) is computed to determine one or more temporallyunstable pixels P_(u) between a new current cluster C_(i) and a priorcluster C_(i-1) from a new prior frame at step 1130, and the key pixelis updated if it is determined in the prior step that the key pixel fromthe prior frame is now unstable. Finally, the cluster map is updatedwith residual pixels only at step 1140, by allowing the cluster to mergewith all other clusters, effectively merging the determined temporallystable pixels with the determined unstable pixels P_(u).

Computing Residual Statistics—The overall concept of residual-onlycompute lends itself to one or more statistics calculations for thecluster map, an essential step in segmentation. Because residualstatistics are maintained on the block level, a first step utilizesblock statistics accumulators, which must be merged after a finalrefinement iteration. Subsequent stages will occur during residualsegmentation to compute statistics solely on temporally unstable pixels.Computing the statistics of a set of data is inherently a serialoperation. Many GPU-based implementations associated with statistics arepresented in Ian E. G. Richardson, H.264/MPEG-4 Part 10 White Paper,2003, where a merge-sort implementation is presented. Eric Sintron andUlf Assarson, “Fast Parallel GPU-Sorting Using a Hybrid Algorithm,”Journal of Parallel and Distributed Computing, vol. 68, no. 10, pp.1381-1388, October 2008. Another approach, presented in Sintron andAssarson, utilizes a linked list prefix computations, implemented onGPUs. In Zheng Wei and Joseph Jaja, “Optimization of Linked List PrefixComputations on Multithreaded GPUs Using CUDA,” in 2010 IEEEInternational Symposium on Parallel & Distributed Processing (IPDPS),Atlanta, 2010, a parallel search is presented however, they are mostlyaimed at a homogeneous data set, with the idea of starting with a largedata set, and then condensing, or reducing the data set withintermediate statistics, until the final statistics are calculated.Other techniques include stream compaction Tim Kaldewey, Jeff Hagen,Andrea Di Blas, and Eric Sedlar, “Parallel Search On Video Cards,” inFirst USENIX Workshop on Hot Topics in Parallelism (HotPar '09), 2009,and scan/scatter algorithms Shubhabrata Sengupta, Mark Harris, YaoZhang, and John D. Owens, “Scan Primitives for GPU Computing,” inProceedings of the 2007 Graphics Hardware Conference, San Diego, Calif.,2007, pp. 97-106. Any of these techniques can work for sorting throughthe clusters and compacting the data into a few segments. In accordancewith the invention, support for sort/compaction across multiple portionsof the inventive algorithm have been provided, since statisticscomputation is very critical for real-time implementation. In such acase, a different approach is taken from that presented in the priorart. The compute load that is associated with the statistics ispreferably distributed so that a parallel implementation becomesfeasible. Note that if APUs are available, an alternative approach couldbe used in which an X-86 processor (for instance), sharing memory with anumber of ALUs, may perform the intermediate calculations. Theperformance would even be enhanced further in that case because a sharedmemory CPU does provide an enhanced ability in handling all the relevantserial operations.

Statistics Implementation—Statistics are first accumulated on the blocklevel into two separate buffers, as is displayed in FIG. 12. FIG. 12depicts a first stage of statistics accumulation, which occurs at theend of the initialization kernel. In this case, preferably each pixel inthe image is assigned a thread 1220, which add the pixel's statistics(red, green, and blue value) to a spatially indexed buffer 1230 at thelocation indicated by the cluster number 1210. Because initial clusternumbers are defined according to spatial coordinates, the thread indexedbuffer 1240 remains empty.

Because the initial cluster numbers are constrained by the spatialboundaries of the block, the initialization kernel will merge thespatial statistics on the block level. The integration of statisticscomponents is integrated into, both, the linking and refinement steps,illustrated in FIG. 13. FIG. 13 depicts the statistics component of thelinking kernel. As in FIG. 12, the cluster number of a given pixel isshown at 1210. However, the upper circle indicates the starting numberand the circle indicated the linked number. 1310 and 1320 highlight theeffect of spatial location on the merging of statistics. At 1310, apixel merges with a segment whose statistics are located in the samespatial buffer 1230, allowing merging within the same buffer. Theopposite case is shown at 1320, where the statistics are moved to thethread indexed buffer 1240. The refinement and linking kernels willhence update these statistics as the clusters merge using the secondarybuffer. A final merge kernel will compact the secondary buffers acrossall blocks and merge the results with the primary spatial buffers tocreate a global statistics map, which is then integrated with the restof the stereo algorithm, displayed in FIG. 14. FIG. 14 depicts thestatistics component of the refinement kernel. Again, the initial andrefined cluster number of a given pixel are shown at 1210. When a pixelis refined 1410, any stats remaining in the spatially indexed buffer1230 are moved to the thread indexed buffer 1240. FIG. 15 depicts theglobal merge of the thread index and spatially indexed statisticsbuffers. At 1510, each thread loops through every cluster number in agiven block. When a match is found 1420, the thread with the lowestindex merges statistics global with the spatially indexed buffer.

Disparity Decomposition—The goal of disparity computation is todecompose the image into a series of surfaces that are present atdifferent disparity values. The right, reference image I_(R) issubtracted from a shifted version of the left image. However, instead oflooking at per-pixel metrics, such as described in accordance with theprior art noted above, in accordance with an embodiment of the inventionutilizes a segment-based disparity estimate, and tries to represent thebest disparity value that is associated with a given segment.

In accordance with this embodiment of the invention, a left image I₁ isshifted one pixel at a time, while subtraction between left and rightimages is performed with the shifted versions of the left image. Everyshift then represents a new disparity. The resulting set of differenceimages then constitutes a disparity decomposition of every segment inthe image. Any zero-pixels represent regions (or segments) in the imagethat are candidates for the correct disparity. The computation of adifference image is presented as:

For any given segment Si, such that S_(i)⊆S,

For a given disparity, d_(i,S):

$\begin{matrix}{\left. \left\{ S_{i} \right\}_{n = 1}^{\overset{\sim}{N}}\longleftarrow{\overset{\sim}{S}}_{i} \right.{and}{{I_{D}^{(d)}\left( {x,y} \right)} = {\max\limits_{c \in {\{{R,G,B}\}}}\left( {{{{I_{R}\left( {x,y} \right)} - {I_{L}\left( {{x - d},y} \right)}}} \leq \tau_{c}} \right)}}{{such}\mspace{14mu}{that}\text{:}}{\left( {x,y} \right) \in \left\{ {\left( {x_{0},y_{0}} \right),\left( {x_{1},y_{1}} \right),\ldots\mspace{14mu},\left( {x_{N},y_{N}} \right)} \right\}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

Where d denotes the current shift, and τ_(c) is the threshold that isassociated with a current color channel.

So, Ñ⊆N

For any given segment, disparity decomposition is a means of reducingthe candidate disparity values that can be associated with the segment(based on a similarity metric). The goal is to determine a disparitythat best matches a given segment. A similarity metric is used in whichthe total number of overlapping pixels can zero out regions in thesegment during the subtraction phase of disparity decomposition. For agiven disparity, the more pixels in the segment that are zeroed out, thecloser the segment is to the correct disparity. The similarity metricfor estimating segment disparity is given by:

$\begin{matrix}{{\overset{\sim}{d}}_{i,S} = {\underset{D}{argmax}{{\overset{\sim}{S}}_{i}\left( {x,y,d} \right)}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$Where {tilde over (D)}_(i,S) represents the disparity estimate for agiven segment S, and {tilde over (S)}_(i)(x,y,D) represents the portionof the segment that is present at step D, in a given disparitydecomposition step. Every segment in the image can be considered asequence that is comprised of the reference image's pixels, such that{S_(n)}_(n=1) ^(N) where N is the size of the segment in the referenceimage. For a given disparity, we can define a sub-sequence {{tilde over(S)}_(n)}_(n=1) ^(Ñ) ^(D) , for a given disparity, D, such that {tildeover (S)}_(n) is a subsequence of {S_(n)}_(n=1) ^(N), and N≥Ñ.

The goal is to have S_(n) and {tilde over (S)}_(n) overlap nearlyentirely at one of the candidate disparity values. The appropriatedisparity is estimated as one that maximizes the ratio of thesubsequence, relative to its sequence. Equation 7 above can actually berepresented as:

$\begin{matrix}{{\overset{\sim}{D}}_{i,S} = {\underset{D}{argmax}\frac{{\overset{\sim}{N}}_{D}}{N}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

FIG. 16 presents an overview of this disparity decomposition section. Asis shown in FIG. 16, processing begins at step 1610 in which left andright source and cluster map image data is read in. Then, at step 1620the right source image value of the key pixel is read to determinesegment temporal stability. At step 1630 an inquiry is made as towhether the condition is stable. If the inquiry at step 1630 is answeredin the affirmative, and it is therefore determined that a stabilitycondition exists, processing continues at step 1660, where the blocklevel counters are merged at the grid level in global memory for eachdisparity and combined globally across the entire image 1670 beforecompressing the resultant decomposition into an image with 1 bitallocated per disparity value.

If, on the other hand, the inquiry at step 1630 is answered in thenegative, and it is therefore determined that a stability condition doesnot exist, processing then continues at step 1640 where differencesbetween left and right images are computed at each disparity level.Next, at step 1650, a cluster counter is incremented if the differencecomputed in step 1640 is below a predetermined threshold determinedempirically. Then, at step 660, these difference results are stored in acompressed form, comprising (1-bit/disparity). So, for 32 disparityvalues, a 32-bit image is used. For 64 disparity values, a 64-bit imageis used, and so on). Finally, processing then passes to step 1660 asdescribed above.

Violating the Subsequence Criterion—The rule, of looking at similarityas an overlap ratio, based on a number of spatio-temporal criteria,presented above can be violated in a number of special cases. Forinstance, this can happen if a smaller segment undergoing disparitydecomposition overlapped a much larger segment with similar spatialcharacteristics. For such a case, penalizing the non-overlapped regionwould present one means of mitigating such a problem. Another case canoccur if such a segment belongs to a textured pattern, occurring at aspatially periodic setting. In this case, agglomeratively growing theregions (see earlier section on segmentation) would present a possiblesolution. Then, textured regions would cluster together before disparitydecomposition occurs (see section below on disparity estimation oftextured regions).

Implementation in CUDA—To accomplish disparity decomposition inaccordance with the various embodiments of the invention, an efficientshift-difference kernel may be implemented, as will be shown below. Eachblock may read one row of source image data to shared memory. As aresult, the difference between left and right image pixels can becomputed for several disparities at once because all of the necessarydata is available in shared memory. Because only one bit is necessary tostore the result of the threshold on the difference result, the resultscan be compressed into a single 32-bit image in which each pixelcontains the results from all disparities. This inventive method notonly reduces storage requirements, but also reduces the number ofrequired read/write operations on global memory. An extension of thismethod can be used to encode 64 bits or 128-bit disparity differences,stored in a 64 or 128-bit image.

The block may be preferably organized in three dimensions with thez-dimension representing the disparity. To maximize performance, thesize of shared memory may preferably be kept under 4 KB to allow up tofour blocks to be swapped out per streaming multiprocessor (SM).Depending on the GPU (or APU), shared memory may differ. In a particularexemplary implementation, the size of the z-dimension will be set toeight and each thread will calculate the difference for four disparityvalues in a loop if the maximum disparity is set to 32. The results ofthe differencing operation are stored in shared memory at the end ofeach iteration, per the description of disparity decomposition that waspresented earlier. After all iterations have been executed, the final32-bit values will be written to Global Memory, as a disparity imageI_(D)(x,y). For larger images, the size of the z-dimension may bereduced to maintain the size of the shared memory used per block.

In addition to computing the difference between the source images, theseintersection pixels are preferably counted with respect to the clustersin the left and right images to determine how much of each cluster inone image intersects with clusters in the other image. FIG. 17 presentssteps describe an implementation of such a counting algorithm, andexpands the counting component of disparity decomposition, depicting theaccumulation of overlapping pixels in shared memory. As is shown in FIG.17, at a first step 1710, counter buffers in shared memory areinitialized to zero. Then, starting at step 1720, for each non-zeroshift-difference pixel perform steps 1730-1750. At step 1730 the bufferis scanned through to find a matching cluster number, and then at step1730, the associated counter is incremented using an atomic sharedmemory operation. Finally, at step 1750, threads in the z-dimensionhandle multiple disparities. After each non-zero shift-difference pixelis addressed at step 1720, processing passes to step 1760 where thevarious threads are synchronized. Finally, at step 1770, the countervalues from the shared memory are added to their respective counters inglobal memory.

A system in which the algorithm of FIG. 17 is implemented is shown inFIG. 18. As is set forth in FIG. 18, one row of left and right clustermap images 1810 are first read into shared memory 1820, each threadpreferably handling four pixels. Then, at 1830 cluster numbersassociated with the read in cluster map images are searched for in theshared memory buffer, 8 pixels being simultaneously searched for usingthreads in the z-dimension. If the cluster number is found, the countassociated with that cluster number is incremented using atomicoperations. Otherwise, the cluster number entry is added and the countassociated with that cluster number is set to one. After all of thecluster map images are read in, the threads are synched so that all thethreads are synchronized, preferably by a GPU or other appropriateprocessing element, during execution, effectively having all threads“wait” until every thread has finished its computation in a block orkernel. Finally at 1840 the current shared memory counts are added tothe existing values in Global Memory.

The buffer containing the cluster numbers in the correct order will becopied to texture memory so it can quickly be accessed by all threadblocks.

Composite Disparity Real-time Analysis—In real-time, the pixelarchitecture is again utilized, such that preferably, only temporallyunstable segments have their disparity computed. As such disparitydecomposition is reduced to segment-disparity decomposition. The resultis a composite disparity image, comprised of already temporally stablesegments and the newly computed/merged temporally unstable segments, onwhich disparity decomposition has been attempted.

Disparity Estimation of Textured Regions—Texture Disparity“Emergence”—The inventive segmentation algorithm is capable ofagglomeratively adjoining spatially disjoint regions through aninter-cluster criterion. Growing such inter-cluster region thresholdsgradually through kernel iterations allows for combining multiplesmaller clusters into one larger, disjoint cluster. The advantage is anability to segment textured regions, which are characterized byspatially periodic regions (see earlier section on texture). Althoughthese regions are clearly disjoint, they are actually easily assembledtogether after disparity decomposition. The best disparity estimateswill solidly remain associated with the correct cluster. This isaccomplished through an emergence of a texture pattern from itsconstituent primitives after disparity computation.

FIG. 19 depicts violating the spatial connectivity criterion in buildingapparently spatially disjoint objects as fundamental building blocks oftexture where texture is composed of two clusters, 1910 and 1920. Allthe white blocks constitute one object 1910, although the individualblocks may themselves be regarded as objects. All the gray blocksconstitute another object 1920.

The concept of emergence in texture segmentation and the subsequentdisparity computation is consistent with Gestalt psychology, orGestaltism. In Gestalt theory, the brain is holistic, withself-organizing features, which, when combined together form morecomplex objects. As such, many Gestalt theorists argue that objectsemerge from their constituent parts, hence the concept of emergence,presented by Metzger, noted above. In the inventive implementation oftexture segmentation, a similar approach is adapted. The inventiveapproach to texture segmentation is Gestalt-inspired, and allows forthis emergence of an object, such as those highlighted in FIGS. 20 and21 from a number of constituent segments. FIG. 20 displays the resultsof disparity decomposition on a checkered texture, showing the inputsource images at 2010, the segmented source at 2020, and the disparitydecomposition at 2030L and 2030R. FIG. 20 includes a disparitydecomposition of a textured checkerboard sequence. Two segments arecreated (one for black squares and one for white ones). The disparity ofboth is estimated. In the disparity map, both objects have the samedisparity computed. Different colors of the checkerboard form differentclusters which appear at the same disparity. Texture “emerges” from thisdecomposition. FIG. 21 displays the results of disparity decompositionon a simulated texture region, showing the input source at 2110, thesegmented source at 2120, and the disparity decomposition at 2130L and2130R. FIG. 21 depicts two checkerboard sequences offset from each otherby a disparity value (18 in this case). Again, the segmentationhighlights two segments and after disparity computation they emerge asone object.

Both figures highlight a highly-textured region where conventionalregion-based and pixel-based disparity computation techniques performpoorly. In both images, a checkerboard sequence is estimated at thecorrect disparity. This is because the checkerboard sequence is reallycomprised of two objects representing all the white squares and all theblack ones. Segmentation is first accomplished on the back and whitesquares separately. These squares are then agglomeratively adjoined tocreate larger white and black clusters. The disparities of each of thetwo objects is then computed. A depth-based clustering step is utilizedto segment a foreground object that is highly textured, comprised of theentire checkerboard sequence.

Therefore, in accordance with various embodiments of the presentinvention, an algorithm for disparity computation that preferably runson a GPU and other appropriate platforms, and performs very well inreal-time conditions is presented. A residual compute portion of thealgorithm reduces the FLOPs count dramatically, by exploiting a residualarchitectural component, and creating composite intermediate images forthe segmentation as well as disparity computation. Texture is mitigatedwith a Gestalt-inspired technique that emphasizes the emergence oftexture at the correct disparity by correctly estimating the disparitiesof its constituents. Examples of mitigating other chronic issuesassociated with region and pixel-based techniques have also been shown.

It will thus be seen that the objects set forth above, among those madeapparent from the preceding description, are efficiently attained and,because certain changes may be made in carrying out the above method andin the construction(s) set forth without departing from the spirit andscope of the invention, it is intended that all matter contained in theabove description and shown in the accompanying drawings shall beinterpreted as illustrative and not in a limiting sense.

It is also to be understood that this description is intended to coverall of the generic and specific features of the invention hereindescribed and all statements of the scope of the invention which, as amatter of language, might be said to fall there between.

What is claimed:
 1. A method for performing segmentation of an image,comprising the steps of: acquiring one or more images by an imagesensor; classifying each pixel in the acquired one or more images as oneof a background pixel, a temporally stable pixel, and a temporallyunstable pixel as compared with a prior image frame; assigning a uniquecluster number only to each pixel in the acquired one or more images bya computer processor that are determined to be temporally unstable, thecluster numbers being assigned in accordance with a predeterminedsequence; connecting a first of the pixels determined to be temporallyunstable with a second of the pixels determined to be temporallyunstable to form a larger cluster and assigning a cluster number fromthe first of the pixels to the second of the pixels by the processor, ifit is determined that the pixels do not differ by more than apredetermined threshold with respect to one or more characteristics; andconnecting the larger cluster with a cluster comprising temporallystable pixels if it is determined that the pixels in the larger clusterdo not differ from the pixels in the cluster comprising the temporallystable pixels by more than a predetermined threshold with respect to oneor more characteristics.
 2. The method of claim 1, wherein thepredetermined sequence comprises an ascending number sequence.
 3. Themethod of claim 2, wherein the assigned cluster number is the lower ofthe two unique cluster numbers.
 4. The method of claim 1, wherein thepredetermined sequence comprises a descending number sequence.
 5. Themethod of claim 4, wherein the assigned cluster number is the higher ofthe two unique cluster numbers.
 6. The method of claim 1, wherein thestep of connecting two pixels is performed for each pair of pixels inthe one or more images located spatially adjacent to each other.
 7. Themethod of claim 1, wherein the step of connecting two pixels isperformed for each pair of pixels in the one or more images locatedwithin a predetermined distance from each other.
 8. The method of claim1, further comprising the steps of: connecting a third pixel determinedto be temporally unstable as compared with the prior image frame to theformed larger cluster; and assigning a cluster number from one of thecluster number of the third pixel and the cluster number of all of thepixels in the larger cluster to each of the third pixel and all of thepixels in the larger cluster, if it is determined that the third pixeland the pixels of the larger cluster do not differ by more than apredetermined threshold with respect to one or more characteristicsthereof.
 9. The method of claim 1, wherein one or more large clusterscomprising at least two pixels similar to each other with respect to oneor more characteristics are formed.
 10. The method of claim 9, furthercomprising the step of defining a key pixel for each larger cluster fromamong the at least two pixels therein, and utilizing that key pixel asthe unique cluster number.
 11. The method of claim 1, further comprisingthe step of connecting a plurality of clusters via their clusternumbers.
 12. The method of claim 11, wherein the plurality of clustersare further connected in accordance with one or more predeterminedthresholds.
 13. The method of claim 11, further comprising the step ofdefining a stopping criterion beyond which connection of the clustersdoes not extend.
 14. The method of claim 1, further comprising the stepsof: segmenting each cluster into sub-regions in accordance with one ormore characteristics of each cluster and pixels thereof; and calculatinga disparity of each of the sub-regions.
 15. The method of claim 14,wherein each of the one or more clusters are segmented into the subregions by breaking up the clusters across rows and columns of pixels inan image.
 16. A computer program stored in a non-transitory storagemedium for performing segmentation of an image, the computer program,when implemented on a processor, causing the processor to perform thesteps of: receiving one or more images from an image sensor; classifyingeach pixel in the received one or more images as one of a backgroundpixel, a temporally stable pixel, and a temporally unstable pixel ascompared with a prior image frame; assigning a unique cluster numberonly to each pixel in the received one or more images that is determinedto be temporally unstable, the cluster numbers being assigned inaccordance with a predetermined sequence; connecting a first of thepixels determined to be temporally unstable with a second of the pixelsdetermined to be temporally unstable to form a larger cluster andassigning a cluster number from the first of the pixels to the second ofthe pixels, if it is determined that the pixels do not differ by morethan a predetermined threshold amount in accordance with one or morecharacteristics thereof; and connecting the larger cluster with acluster comprising temporally stable pixels if it is determined that thepixels in the larger cluster do not differ from the pixels in thecluster comprising the temporally stable pixels by more than apredetermined threshold with respect to one or more characteristics. 17.The computer program of claim 16, wherein the predetermined sequencecomprises an ascending number sequence.
 18. The computer program ofclaim 17, wherein the assigned cluster number is the lower of the twounique cluster numbers.
 19. The computer program of claim 16, whereinthe predetermined sequence comprises a descending number sequence. 20.The computer program of claim 19, wherein the assigned cluster number isthe higher of the two unique cluster numbers.
 21. The computer programof claim 16, wherein the step of connecting two pixels is performed foreach pair of pixels in the one or more images located spatially adjacentto each other.
 22. The computer program of claim 16, wherein the step ofconnecting two pixels is performed for each pair of pixels in the one ormore images located within a predetermined distance from each other. 23.The computer program of claim 16, further comprising the steps of:connecting a third pixel determined to be temporally unstable ascompared with the prior image frame to the formed larger cluster; andassigning a cluster number from one of the cluster number of the thirdpixel and the cluster number of all of the pixels in the larger clusterto each of the third pixel and all of the pixels in the larger cluster,if it is determined that the third pixel and the pixels of the largercluster do not differ by more than a predetermined threshold withrespect to one or more characteristics thereof.
 24. The computer programof claim 16, wherein one or more large clusters comprising at least twopixels similar to each other with respect to one or more characteristicsare formed.
 25. The computer program of claim 24, further comprising thestep of defining a key pixel for each larger cluster from among the atleast two pixels therein, and utilizing that key pixel as the uniquecluster number.
 26. The computer program of claim 16, further comprisingthe step of connecting a plurality of clusters via their clusternumbers.
 27. The computer program of claim 26, wherein the plurality ofclusters are further connected in accordance with one or morepredetermined thresholds.
 28. The computer program of claim 27, furthercomprising the step of defining a stopping criterion beyond whichconnection of the clusters does not extend.
 29. The computer program ofclaim 16, further comprising the steps of: segmenting each cluster intosub-regions in accordance with one or more characteristics of eachcluster and pixels thereof; and calculating a disparity of each of thesub-regions.
 30. The computer program of claim 29, wherein each of theone or more clusters is segmented into the sub regions by breaking upthe clusters across rows and columns of pixels in an image.